The invention relates to a method for the monitoring of a plant having a plurality of sub-systems in accordance with the preamble of the independent claim.
Plants such as hydroturbines or gas turbines with driving generators for the generation of electrical energy in power stations, turbocompressors and piston compressors to compress gases, pump stations or aeronautical engines are typically very complex systems which frequently work at different working points and whose respective operating status is influenced by a large number of process parameters. It is necessary, as a rule, to monitor the status of such plants in order to detect operating malfunctions, that is deviations from normal operating characteristics, at the earliest possible time or to monitor the state of wear of individual components so that necessary maintenance work can be planned in time and carried out efficiently.
It is, for example, possible for this purpose to inspect the plant at regular intervals. However, it is usually necessary to switch the plant off for this, which is disadvantageous from an economic aspect.
A plurality of process parameters such as, for example, pressure, temperatures at different points of the plant, flow rates, speeds, power, bearing temperatures, etc. is frequently determined by measurement and, for example, stored as a function of time or presented in graphical form. Such plants do not, however, usually work at a fixed operating or working point so that the time-dependent course of the measured parameters detected for the monitoring also shows great fluctuations in normal operation, that is operation free from malfunction. It is therefore only possible to judge whether the plant is working without malfunction with great difficultyxe2x80x94if at allxe2x80x94by using the time-dependent course of the measured parameters. While fixed threshold values can be set for some process parameters detected by measurement, with an alarm being triggered if these are not reached or exceeded, this procedure also causes the disadvantage due to the variable working points that false alarms are frequent or that actually existing operating malfunctions are not recognised in time. It is furthermore difficult to recognise and evaluate changes which take place very slowly over time such as occur, for example, due to operation-related wear.
It would be possible in principle to make a physical model of the whole plant, that is to calculate the physical relationships between the individual process parameters and then to carry out an evaluation of the operating status by a comparison of such physical model calculations and the parameters detected by measurement. However, this approach is frequently much too cost-intensive and complex in practice so that it is less suitable, in particular for industrial applications. One reason for this is that such plants have an enormous complexity with a plurality of sub-systems in mutual interaction with one another so that a more or less reliable physical model must consider a plurality of relationships between the individual process parameters, whereby its preparation is made into an extremely difficult task which is both time and cost intensive.
For this reason, a monitoring method is proposed in EP-A-0 895 197 which is based on an experimental modelling, that is without using a prior calculation of the physical relationships between the process parameters. Respective measured values are detected for a fixed set of process parameters at pre-settable time intervals. Measured values are detected for the process parameters for as many different working points as possible in a first, so-called modelling phase, with a check being made that the plant is working without malfunction during this modelling phase. An experimental model for the operating characteristics is prepared using the measured values detected during the modelling phase, with the input variables of the model being at least a part of the set of process parameters and with the output values comprising a model value for at least one of the process parameters. A respective residual value is determined by a comparison of the respective model value and the actual measured value of the modelling phase corresponding thereto and the model is optimised by determining model parameters such that a model error determinable from the residual values becomes minimal. A simple mathematical relationship, which as a rule has no physical significance, is usually selected as the model structure. When the modelling phase has been completed, the experimental model has thus xe2x80x9clearnedxe2x80x9d how the plant, i.e. the individual process parameters, behaves at different working points.
In the second phase, the normal operating phase of the plant, at least one monitoring parameter, which is independent of the respective current working point, is determined at pre-settable time intervals using the model for the operating characteristics. This monitoring parameter is preferably the residual value resulting from the difference between the respective current measured value and the model value corresponding thereto. The time-dependent course of the monitoring parameter is used to evaluate the wear in the sub-systems of the plant and/or to detect operating malfunctions.
The method disclosed in EP-A-0 895 197 has the advantage that it essentially works without physical modelling and is therefore very simple and suitable for industrial applications. Moreover, it takes into account the respective current working point of the plant and can also recognise slowly progressing changes such as are caused, for example, by wear, at an early point. Complex plants can also be reliably monitored in this way. Furthermore, an efficient planning of the maintenance work is possible, which results in a reduction of maintenance and operating costs.
Even though the method in accordance with EP-A-0-895 197 has proved itself in practice, there is nevertheless a need for improvement. The isolation and identification of faulty process parameters is actually relatively difficult and costly. An unrecognised fault in a process parameter can result in the triggering of a false alarm, which represents a limitation, in particular under economic aspects.
It is therefore an object of the invention to modify and improve such a method for the monitoring of a plant having a plurality of sub-systems such that a fault in a process parameter is reliably recognisable, such that the faulty process parameter can be identified more easily and such that false alarms are avoided as much as possible.
The method which satisfies this object for the monitoring of a plant having a plurality of sub-systems which is operable at variable working points is characterised by the features of the independent claim. Advantageous measures and preferred embodiments of the invention can be seen from the dependent claims.
The method in accordance with the invention therefore comprises the following steps: respective measured values are detected for a fixed set of process parameters at pre-settable time intervals during the operation of the plant. The measured values detected in a learning phase for different working points are used to prepare models for the operating characteristics of the sub-systems, with the input values of each model being at least a part of the process parameters and the output value of each model comprising a model value for at least one of the process parameters, and with the models being optimised by comparing the model values with the measured values. At least one monitoring parameter, which is independent of the respective current working point, is determined at pre-settable time intervals in an operating phase using the models and the time-dependent course of the monitoring parameter is used for the monitoring of the plant. A pre-check is carried out prior to the determination of the monitoring parameter, in which a check is made whether at least the measured values for those process parameters which are operating parameters are within a pre-determined range.
The method in accordance with the invention is therefore based on the measured values for the operating parameters and/or the environmental parameters being subjected to a pre-check before they are used as input values for the specific models for the different sub-systems. Operating parameters are here understood to mean those process parameters which describe the operating status of the whole plant, i.e. which are not specific to an individual sub-system. Environmental parameters are understood to be those process parameters which are not influenced, at least not approximately influenced, by the operation of the plant, for example the air temperature outside the plant. Since at least one operating parameter is, as a rule, used in each specific model for the operating characteristics of a sub-system, the pre-check of the measured values for the operating parameters allows the use of faulty input values for the models of the sub-systems to be prevented. It is therefore ensured that only such models are evaluated for the sub-systems which have non-malfunctioning environmental and operating parameters as input values.
The diagnostic capability of the method is also substantially improved by this measure, since if now a deviation from the normal operating characteristics, e.g. a malfunction, an error or high wear, is detected by means of a model for a sub-system, it is certain that the error or the wear is actually present in this sub-system and that it is not due to a fault in an operating parameter. The probability of a false alarm is thus dramatically reduced.
As the operating parameters and/or the environmental parameters are examined for errors in the pre-check and the sub-systems are only then checked against the operating parameters and environmental parameters classified as free of malfunction, a simpler isolation of errors or error identification is also possible. That is, if the error is revealed in the pre-check, then one of the measured values of the operating parameters must be faulty, or an error has occurred in the determination of a measured value for an environmental parameter.
The method in accordance with the invention therefore carries out the monitoring more or less in two parts. First, the operating parameters which describe the operating status of the whole plant are examined in the pre-check and only then are the specific models for the operating characteristics of the sub-systems evaluated against the operating parameters classified as free of malfunctions. A reliable distinction can thus be made as to whether the deviation from the normal operating characteristics is caused only by one sub-system or by a plurality of sub-systems or by a fault associated with an operating parameter or an environmental parameter.
A machine model is preferably determined for the pre-check whose input values are operating parameters and whose output values comprise a machine model value for at least one of the operating parameters. Such a machine model actually represents a relatively simple and reliable method of checking the operating parameters for faults.
It is of advantage here if the machine model comprises at least one model with which a machine model value for the deviation of the measured value for an operating parameter is determined from a theoretically determined desired value for this operating parameter. The deviation of the respective current measured value from a theoretically determined desired value is therefore modelled by means of this model. Then this model value for the deviation is compared with the actual value of the deviation, it namely having been found that the deviation of the measured value from the theoretical value is substantially more easy to model and that such a model furnishes more reliable data.
The model for the deviation of the measured value from the theoretically determined desired value is preferably an experimental model which is prepared and optimised using the measured values detected in the learning phase.
The machine model can be prepared in the same or a similar way to the models for the operating characteristics of the sub-system. The machine model can therefore in particular be prepared using the measured values which are detected for various working points in the learning phase, with a deviation value being determined by a comparison of the machine model value with the actual measured value corresponding thereto, and with model parameters of the machine model being optimised such that a model error determinable from the deviation values becomes minimal.
The environmental parameters are preferably also examined for freedom from malfunction during the pre-check, for example by redundant measurement or by plausibility checks.
It is furthermore advantageous for the pre-check to comprise a range test in which a check is made whether individual operating parameters or combinations thereof are within the range of working points learned in the learning phase. It can actually be prevented in this way that xe2x80x9cunlearnedxe2x80x9d states of the plant result in false warnings.
After the pre-check in the preferred embodiment, only the models for the sub-systems are evaluated in each case in which only process parameters are used for which no error or no malfunction is detected in the pre-check. Unnecessary calculation or evaluation time can be avoided with this measure.
It is a further advantageous measure if a confidence range is fixed at least for one monitoring parameter, if this confidence range can be extended if the monitoring parameter leaves the original confidence range, and if the extension of the confidence range is registered. The operator""s attention is drawn to wear phenomena, for example, at an early point by this measure so that maintenance work can be scheduled in time.